We observe that the regressed SMPL meshes roughly capture the human pose and shape but do not always align with the VisDB predictions in details.
Therefore, we use the regressed parameters as initialization and propose efficient test-time optimization to further optimize the SMPL parameters against VisDB predictions.
For this optimization, we apply similar losses as in Eq. (REF )-(REF ), except that the ground-truths \(\lbrace {V_c}^*,{J_c}^*\rbrace \)  are replaced by the VisDB predictions converted into root-relative coordinates in the camera space.
Please refer to the supplemental material for details on estimating the root joint coordinate in the camera space.
Since we initialize the SMPL parameters by the regression network and the use strong supervisory signal, i.e., 3D joint and vertex coordinates, the test-time optimization only takes around 100 iterations to converge using an Adam optimizer [1]}.
We illustrate the process of SMPL regression and optimization in Figure REF  (right).
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